With the proliferation of video formats and devices, changes in image size, registration and cropping may occur more frequently. Video reformatting for repurposing may also be more common than in the past. For example, sending a 720 sample per line 601 SD (Standard Definition) video signal as a 704 ATSC (Advanced Television Systems Committee) digital broadcast signal would require reformatting. As another example, conversion of a 720 SD video signal to a 1920 HD (High Definition) video signal, which may be necessitated by an SD and HD simulcast, may also require reformatting. In addition to reformatting that may arise due to changing broadcast options, conversion of video format for consumption on mobile devices and other handheld devices, for example, conversion of HD to QCIF (Quarter Common Image Format) may require reformatting. This reformatting may require a change in image size, a spatial shift, also referred to as spatial registration, a loss of image content near the image borders, also referred to as cropping, or other formatting changes. Such reformatting may require that an image fit into a new aspect ratio, for example 16:9 versus 4:3, denoting a width-to-height ratio. Reformatting may require truncation, or cropping, on the sides of an image, or adding blank border segments on the sides, also referred to as the side panels, of an image, or similarly the same on the top and bottom of the image, for example, in the case of letterbox images. Reformatting may present problems for equipment manufacturers, broadcasters, editors and other video professionals. Processing equipment may be set in incorrect modes or may malfunction, or standards may vary, for example, as in the 720 pixel to 704 pixel example above.
A measurement instrument capable of executing a method of measuring spatial distortion, scale, offset or shift, and cropping of video output may be useful. In addition, picture measurements may benefit from spatial alignment between a video test sequence image and a video reference sequence image for full-reference measurements.
In the past, alignment has been achieved, at least in part, using a proprietary stripe place over the original video image. This is intrusive and requires that the test and reference video both have the stripe, which requires that the stripe be added prior to video compression or other processing required for transmission or storage. In some applications, it is not practical or desirable to add the stripe once the need for measurement arises, and this has been a limitation for automated picture quality measurement applications.
An automated method of measuring spatial distortion for automated video measurement (VM) applications such as consumer electronics video output verification may be useful. A method for automated spatial alignment for use in connection with automatic picture quality (PQ) measurements may also be useful. It may be desirable that the method be robust in the presence of digital compression artifacts, random noise, quantization error, non-linear distortion, linear distortion, interference and other process which may impair the quality of a video signal. It may also be desirable for this method to operate without prior knowledge of the video content, including any stripe added to the video signal, aspect ratio, DUT (Device Under Test) pixel clock, or other indications of the likely horizontal or vertical scaling, offset or cropping.
Additionally, it may be desirable that the method be both accurate and computationally efficient. With improvements in codec (coder/decoder) technology, primarily in the deployment and improvements in H.264 codecs, 1920 pixel by 1080 pixel HD video increasingly tends to have major objectively measured error that causes very poor correlation in details, while appearing to a viewer, under normal viewing conditions, nearly the same as the original reference video. In particular, improved accuracy for measuring spatial distortion for HD video codecs, for example, the H.264 codec, and other applications where pixel-level test video may be impaired may be beneficial. The pixel-level test video may be impaired to the point of having low localized correlation while appearing, under normal viewing conditions, to an average viewer to be of good quality. A computationally efficient and accurate method that additionally maintains the useful features described above may be advantageous.